@article {Jain2007,
	title = {Intra-operative 3D guidance in prostate brachytherapy using a non-isocentric C-arm},
	journal = {Medical image computing and computer-assisted intervention (MICCAI)},
	volume = {10},
	number = {Pt 2},
	year = {2007},
	pages = {9{\textendash}17},
	publisher = {Johns Hopkins University, USA},
	abstract = {<p>Intra-operative guidance in Transrectal Ultrasound (TRUS) guided prostate brachytherapy requires localization of inserted radioactive seeds relative to the prostate Seeds were reconstructed using a typical C-arm, and exported to a commercial brachytherapy system for dosimetry analysis Technical obstacles for 3D reconstruction on a non-isocentric C-arm included pose-dependent C-arm calibration; distortion correction; pose estimation of C-arm images; seed reconstruction;, C-arm to TRUS registration In precision-machined hard phantoms with 40-100 seeds, we correctly reconstructed 99 8\% seeds with a mean 3D accuracy of 0 68 mm In soft tissue phantoms with 45-87 seeds, clinically realistic 15 degrees C-arm motion, we correctly reconstructed 100\% seeds with an accuracy of 1 3 mm The reconstructed 3D seed positions were then registered to the prostate segmented from TRUS In a Phase-1 clinical trial, so far on 4 patients with 66-84 seeds, we achieved intra-operative monitoring of seed distribution, dosimetry We optimized the 100\% prescribed iso-dose contour by inserting an average of 3 75 additional seeds, making intra-operative dosimetry possible on a typical C-arm, at negligible additional cost to the existing clinical installation</p>
},
	keywords = {Algorithms, Brachytherapy, Computer-Assisted, Equipment Design, Equipment Failure Analysis, Humans, Imaging, instrumentation/methods, Interventional, Intraoperative Care, Male, methods, Prostatic Neoplasms, Prosthesis Implantation, Radiographic Image Enhancement, Radiographic Image Interpretation, Radiography, radiography/radiotherapy, Radiotherapy, Reproducibility of Results, Sensitivity, Specificity, Three-Dimensional, Tomography, X-Ray Computed},
	author = {Ameet K. Jain and Anton Degeut and Iulian Iordachita and Gouthami Chintalapani and Jack Blevins and Le Yi and Elwood Armour and E. Clif Burdette and Danny Y Song and Gabor Fichtinger}
}
@article {Jain2006a,
	title = {C-arm tracking and reconstruction without an external tracker},
	journal = {Medical image computing and computer-assisted intervention (MICCAI)},
	volume = {9},
	number = {Pt 1},
	year = {2006},
	pages = {494{\textendash}502},
	publisher = {Department of Computer Science, Johns Hopkins University, USA},
	abstract = {<p>For quantitative C-arm fluoroscopy, we have developed a unified mathematical framework to tackle the issues of intra-operative calibration, pose estimation, correspondence, reconstruction, without the use of optical/electromagnetic trackers or precision-made fiducial fixtures Our method uses randomly distributed unknown points in the imaging volume, either naturally present or induced by randomly sticking beads or other simple markers in the image pace After these points are segmented, a high dimensional non-linear optimization computes all unknown parameters for calibration, C-arm pose, correspondence, reconstruction Preliminary phantom experiments indicate an average C-arm tracking accuracy of 0 9 degrees, a 3D reconstruction error of 0 8 mm, with an 80 region of convergence for both the AP, lateral axes The method appears to be sufficiently accurate for many clinical applications, and appealing since it works without any external instrumentation, does not interfere with the workspace</p>
},
	keywords = {Algorithms, Calibration, Computer-Assisted, Equipment Design, Equipment Failure Analysis, Fluoroscopy, Imaging, instrumentation/methods, methods, Radiographic Image Enhancement, Radiographic Image Interpretation, Reproducibility of Results, Sensitivity, Specificity, Three-Dimensional},
	author = {Ameet K. Jain and Gabor Fichtinger}
}
